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Easing Malaysia’s traffic woes through a smarter transport

By Kamal Brar vice president and general manager of APAC, Hortonworks

Economic progress can seem like a two-edged sword – we relish the opportunities for career and lifestyle choices offered by our expanding cities, but urban transportation woes can sometimes make us wonder if it’s all worth it.

The Association of Southeast Asian Nations (ASEAN) recently celebrated its 50th anniversary this year – and there is much to be proud of. In simple economic terms, ASEAN’s GDP growth over that period from $37.6 billion to $2.6 trillion has set the region firmly on the path to advanced standards of living. But this progress has undeniably come with a price.

Traffic Woes in Malaysia

Malaysia’s capital city Kuala Lumpur can rightly claim to finally be taking action to address its traffic problems. However, despite substantial recent investment in an MRT system, the city’s roads continue to be plagued by traffic jams.

The World Bank estimates that Greater Kuala Lumpur residents “spend more than 250 million hours a year stuck in traffic.” Congestion exacts a huge toll on the country’s economy – Greater Kuala Lumpur traffic consumed between 1.1% and 2.2% of GDP in 2014.

One key reason for these chronic traffic jams is the sheer number of cars on the road. Historic political support for the domestic automotive industry led to cheap cars and subsidised fuel prices, with the remarkable result that Malaysia has the third highest car ownership rate in the entire world, according to a Nielsen survey. 93 percent of Malaysia’s households have at least one car.

This promotion of private vehicle ownership came together with a dismal level of investment in public transportation, resulting in the sad fact that just 17 percent of Kuala Lumpur commuters use public transport, according to the World Bank report. This contrasts with Singapore at 62 percent and Hong Kong’s impressive figure of 89 percent.

Kuala Lumpur’s commuting public has had a limited choice of transport. The city has six metro lines run by four different operators, including four light rail transit systems and a monorail serving as a people mover. KL also has a bus system which compares poorly to those of other regional cities. Efforts are underway to redraw the bus network to increase ridership and improve the overall public transport system.

Recognising that serious action had to be taken, the national government approved plans for an MRT system in Kula Lumpur, and construction of the first line began in July 2011. Operations begn in 2016. The system is planned to have three lines and will represent the largest infrastructure project ever undertaken in Malaysia.

One of the lines, the underground Circle Line, will loop around the city and serve an important role to tie-up and integrate the currently disjointed LRT and monorail lines.

Data aggregation and analysis will clearly be key in the success of a project as complex as creating an integrated mass transit system for a huge and growing city like Kuala Lumpur.

The Malaysian authorities have clearly recognized that transportation nightmares are a serious threat to the functioning of the capital city, and have finally taken steps to follow Singapore’s lead in developing mass transit projects. However, private automobile traffic is not going away any time soon, and one challenge faced by city administrations is how to manage it and how to integrate it into a comprehensive urban transit system.

Poster Boy – Singapore

The poster boy for urban traffic management is definitely Singapore, which in 1998 pioneered the world’s first Electronic Road Pricing (ERP) system. This congestion pricing system automatically deducts the toll via a pre-paid in-vehicle unit, electronically triggered when the vehicle passes under a purpose-built gantry.

Singapore is now field-testing another world first – an ERP system based on satellite navigation technology instead of physical gantries. The system will have island-wide coverage, and will charge for actual distance travelled. It can also facilitate coupon-less street parking and will provide all road users with real-time traffic information through an intelligent onboard unit.

The benefits to road users and authorities alike from such an advanced system are clearly enormous, and the key to making it possible is our growing ability to gather and analyse massive quantities of data.

Smarter Transport

Transportation and traffic management make a perfect subject for Big Data analysis. The real promise of this burgeoning technology in the transportation sector is its potential to enable a truly comprehensive city-wide transit system, embracing and co-ordinating public and private, road and rail.

Elements of such applications are already underway. In addition to improving the flow and regulation of private automobile traffic, transport authorities around the world are using data analysis to manage and improve mass public transit systems, both bus and rail. Applications include everything from accurate ridership forecasts, to route planning and frequency, to cost-saving maintenance schedules.

The intimate understanding of customer behaviour and journey plans furnished by big data allows authorities to plan for additional services on the routes, such as conveniently located retail stores. It also lets the transit authority tailor communications with each individual rider to notify them of any service changes, upcoming events or weather issues that may impact service, or even provide targeted advertising.

The overall improvement in the commuter’s journey experience delivered by the insightful use of Big Data will lead to enhanced customer satisfaction and help increase train ridership, while providing authorities with new revenue sources.

Learning from others

A significant element of the costs of any mass transit system is maintenance. By leveraging Big Data, authorities can predict optimal maintenance requirements of the equipment – whether trains and their tracks or bus assets.

Data from the sensors installed on the equipment can be analyzed faster and at a more granular level. This helps predict upcoming faults at the individual component level such as brakes, a stretch of rails etc. Authorities can then schedule maintenance of the equipment at precisely the right time, optimising cost and minimising disruption.

One public rail transport provider in the US has successfully deployed Big Data to schedule its equipment maintenance with astonishing results – mean time to failure of the equipment has been reduced by almost 80-90% and equipment life increased by 200%. This has further improved customer safety and satisfaction, enhanced equipment utilization and reduced operating costs.

In another example, the Metro Transit of St Louis (MTL) had always taken maintenance seriously but lacking detailed data on how bus components were actually performing, maintained vehicles retroactively. It replaced parts after they failed, or simply bought new buses.

This approach assured passenger safety and service reliability, but the team suspected that it often replaced parts or discarded entire buses even when replacement wasn’t necessary.

MTL turned to Big Data analysis to better predict when a component on a particular bus will fail, allowing them to proactively service the bus prior to any component failures.

The results have been spectacular – the average time between bus failures has improved by a factor of five. MTL was able to run the buses for much longer, thereby increasing the return on investment in their fleet. Previously, buses were being retired after 35,000 miles per year at 12 years, but now MTL is able to continue using the buses up to 60,000 to 70,000 miles per year at 15 years. This is a 2x improvement on mileage and 30% increase in bus lifespan.

These improvements in vehicle maintenance have saved St Louis area taxpayers more than US$2.5 million per year.

Imagine a time when our cities can boast efficient, cost-effective mass transit buses and trains combined with well-managed highways and streets for private traffic! Given a combination of political will, financial resources and Big Data analysis, this scenario could be nearer than we think.